Checking whether there is an H2O instance running at http://localhost:54321 ..... not found.
Attempting to start a local H2O server...
  Java Version: openjdk version "11.0.11" 2021-04-20; OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.18.04); OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.18.04, mixed mode, sharing)
  Starting server from /usr/local/lib/python3.7/dist-packages/h2o/backend/bin/h2o.jar
  Ice root: /tmp/tmptv739hf5
  JVM stdout: /tmp/tmptv739hf5/h2o_unknownUser_started_from_python.out
  JVM stderr: /tmp/tmptv739hf5/h2o_unknownUser_started_from_python.err
  Server is running at http://127.0.0.1:54321
Connecting to H2O server at http://127.0.0.1:54321 ... successful.

H2O_cluster_uptime:	03 secs
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.32.1.4
H2O_cluster_version_age:	10 days
H2O_cluster_name:	H2O_from_python_unknownUser_7zikra
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.172 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	accepting new members, healthy
H2O_connection_url:	http://127.0.0.1:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
H2O_API_Extensions:	Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
Python_version:	3.7.11 final

Parse progress: |█████████████████████████████████████████████████████████| 100%
AutoML progress: |████████████████████████████████████████████████████████| 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	auc	logloss	aucpr	mean_per_class_error	rmse	mse	training_time_ms	predict_time_per_row_ms	algo
GBM_grid__1_AutoML_20210719_091245_model_1	0.79592	0.254477	0.219521	0.273997	0.271719	0.073831	272	0.043584	GBM
GBM_grid__1_AutoML_20210719_091245_model_2	0.786609	0.288506	0.183858	0.297528	0.287153	0.0824567	296	0.042066	GBM
XGBoost_grid__1_AutoML_20210719_091245_model_6	0.78225	0.250349	0.211155	0.249281	0.266756	0.071159	141	0.028961	XGBoost
XGBoost_grid__1_AutoML_20210719_091245_model_2	0.781446	0.258267	0.230984	0.282165	0.271174	0.0735355	208	0.028001	XGBoost
XGBoost_grid__1_AutoML_20210719_091245_model_5	0.771542	0.256891	0.197349	0.269807	0.270226	0.0730219	156	0.021606	XGBoost
StackedEnsemble_BestOfFamily_AutoML_20210719_091245	0.770103	0.255045	0.241046	0.298502	0.268917	0.0723163	479	0.14578	StackedEnsemble
XGBoost_grid__1_AutoML_20210719_091245_model_1	0.76858	0.328	0.193323	0.295708	0.295434	0.0872812	203	0.018091	XGBoost
XGBoost_grid__1_AutoML_20210719_091245_model_4	0.766887	0.310873	0.1851	0.309675	0.289033	0.0835403	161	0.017711	XGBoost
XGBoost_grid__1_AutoML_20210719_091245_model_8	0.765913	0.259283	0.172262	0.341163	0.269354	0.0725518	174	0.014107	XGBoost
GBM_5_AutoML_20210719_091245	0.756264	0.254962	0.188046	0.271204	0.267183	0.0713867	195	0.019505	GBM
GBM_grid__1_AutoML_20210719_091245_model_3	0.753682	0.255442	0.183356	0.269807	0.266775	0.0711691	186	0.02196	GBM
GBM_1_AutoML_20210719_091245	0.752497	0.272226	0.192882	0.31086	0.277457	0.0769822	196	0.018151	GBM
XGBoost_grid__1_AutoML_20210719_091245_model_7	0.748773	0.307267	0.178202	0.269807	0.291993	0.0852597	189	0.016829	XGBoost
XGBoost_grid__1_AutoML_20210719_091245_model_9	0.748138	0.271588	0.174836	0.311283	0.272789	0.0744139	84	0.01434	XGBoost
GBM_2_AutoML_20210719_091245	0.741366	0.262485	0.196379	0.29592	0.269972	0.072885	246	0.016641	GBM
XGBoost_3_AutoML_20210719_091245	0.741324	0.275359	0.172233	0.332783	0.28101	0.0789664	129	0.015364	XGBoost
GBM_grid__1_AutoML_20210719_091245_model_4	0.736711	0.263309	0.178584	0.329016	0.270405	0.073119	131	0.016817	GBM
XGBoost_2_AutoML_20210719_091245	0.736499	0.266415	0.167425	0.310098	0.271449	0.0736844	173	0.014511	XGBoost
StackedEnsemble_AllModels_AutoML_20210719_091245	0.735568	0.268994	0.155055	0.306882	0.273829	0.0749822	365	0.141358	StackedEnsemble
GBM_3_AutoML_20210719_091245	0.734256	0.268517	0.170837	0.283562	0.275454	0.075875	168	0.026255	GBM

Confusion Matrix

Confusion matrix shows a predicted class vs an actual class.

GBM_grid__1_AutoML_20210719_091245_model_1

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2557828965438706:

		false	true	Error	Rate
0	false	351.0	7.0	0.0196	(7.0/358.0)
1	true	8.0	25.0	0.2424	(8.0/33.0)
2	Total	359.0	32.0	0.0384	(15.0/391.0)

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Confusion Matrix

Confusion matrix shows a predicted class vs an actual class.

GBM_grid__1_AutoML_20210719_091245_model_1

Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.2557828965438706:

		false	true	Error	Rate
0	false	351.0	7.0	0.0196	(7.0/358.0)
1	true	8.0	25.0	0.2424	(8.0/33.0)
2	Total	359.0	32.0	0.0384	(15.0/391.0)

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Checking whether there is an H2O instance running at http://localhost:54321 . connected.

H2O_cluster_uptime:	1 hour 36 mins
H2O_cluster_timezone:	Etc/UTC
H2O_data_parsing_timezone:	UTC
H2O_cluster_version:	3.32.1.4
H2O_cluster_version_age:	10 days
H2O_cluster_name:	H2O_from_python_unknownUser_7zikra
H2O_cluster_total_nodes:	1
H2O_cluster_free_memory:	3.025 Gb
H2O_cluster_total_cores:	2
H2O_cluster_allowed_cores:	2
H2O_cluster_status:	locked, healthy
H2O_connection_url:	http://localhost:54321
H2O_connection_proxy:	{"http": null, "https": null}
H2O_internal_security:	False
H2O_API_Extensions:	Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
Python_version:	3.7.11 final

Parse progress: |█████████████████████████████████████████████████████████| 100%
AutoML progress: |████████████████████████████████████████████████████████| 100%

Leaderboard

Leaderboard shows models with their metrics. When provided with H2OAutoML object, the leaderboard shows 5-fold cross-validated metrics by default (depending on the H2OAutoML settings), otherwise it shows metrics computed on the frame. At most 20 models are shown by default.

model_id	mean_residual_deviance	rmse	mse	mae	rmsle	training_time_ms	predict_time_per_row_ms	algo
StackedEnsemble_AllModels_AutoML_20210719_104903	17648.9	132.849	17648.9	88.6208	0.163532	262	0.100085	StackedEnsemble
StackedEnsemble_BestOfFamily_AutoML_20210719_104903	18040.2	134.314	18040.2	90.7586	0.164731	170	0.038168	StackedEnsemble
GBM_grid__1_AutoML_20210719_104903_model_1	26298.8	162.169	26298.8	112.076	0.175313	348	0.030053	GBM
XGBoost_grid__1_AutoML_20210719_104903_model_4	26312.2	162.21	26312.2	114.551	0.153739	734	0.008871	XGBoost
XGBoost_grid__1_AutoML_20210719_104903_model_3	26845.6	163.846	26845.6	116.373	0.144884	560	0.006384	XGBoost
GBM_3_AutoML_20210719_104903	28199.2	167.926	28199.2	108.219	0.180605	329	0.017913	GBM
XGBoost_grid__1_AutoML_20210719_104903_model_5	33015.2	181.701	33015.2	129.204	0.125457	182	0.005574	XGBoost
GBM_1_AutoML_20210719_104903	39761.3	199.402	39761.3	141.463	0.174056	324	0.012657	GBM
XGBoost_grid__1_AutoML_20210719_104903_model_2	40280.3	200.7	40280.3	144.697	0.155139	427	0.007858	XGBoost
XGBoost_3_AutoML_20210719_104903	40660.6	201.645	40660.6	145.186	0.17298	516	0.004632	XGBoost
XGBoost_1_AutoML_20210719_104903	41158.9	202.876	41158.9	145.477	0.151495	423	0.005749	XGBoost
XGBoost_grid__1_AutoML_20210719_104903_model_1	45457.6	213.208	45457.6	148.708	0.18636	831	0.008415	XGBoost
XGBoost_grid__1_AutoML_20210719_104903_model_6	47667.2	218.328	47667.2	156.676	0.182955	132	0.007236	XGBoost
GBM_4_AutoML_20210719_104903	49454.1	222.383	49454.1	148.902	0.204376	239	0.012723	GBM
XGBoost_2_AutoML_20210719_104903	51269.3	226.427	51269.3	160.919	0.195577	148	0.005407	XGBoost
GBM_grid__1_AutoML_20210719_104903_model_2	61496.6	247.985	61496.6	178.095	0.190502	580	0.025635	GBM
GBM_2_AutoML_20210719_104903	80347.5	283.456	80347.5	195.946	0.22421	172	0.008235	GBM
DRF_1_AutoML_20210719_104903	80365.7	283.488	80365.7	181.736	0.219054	342	0.006979	DRF
XRT_1_AutoML_20210719_104903	90245.7	300.409	90245.7	196.584	0.22948	237	0.004242	DRF
DeepLearning_1_AutoML_20210719_104903	135147	367.623	135147	275.214	0.221364	132	0.004246	DeepLearning

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Variable Importance

The variable importance plot shows the relative importance of the most important variables in the model.

Variable Importance Heatmap

Variable importance heatmap shows variable importance across multiple models. Some models in H2O return variable importance for one-hot (binary indicator) encoded versions of categorical columns (e.g. Deep Learning, XGBoost). In order for the variable importance of categorical columns to be compared across all model types we compute a summarization of the the variable importance across all one-hot encoded features and return a single variable importance for the original categorical feature. By default, the models and variables are ordered by their similarity.

Model Correlation

This plot shows the correlation between the predictions of the models. For classification, frequency of identical predictions is used. By default, models are ordered by their similarity (as computed by hierarchical clustering). Interpretable models, such as GAM, GLM, and RuleFit are highlighted using red colored text.

SHAP Summary

SHAP summary plot shows the contribution of the features for each instance (row of data). The sum of the feature contributions and the bias term is equal to the raw prediction of the model, i.e., prediction before applying inverse link function.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.

Residual Analysis

Residual Analysis plots the fitted values vs residuals on a test dataset. Ideally, residuals should be randomly distributed. Patterns in this plot can indicate potential problems with the model selection, e.g., using simpler model than necessary, not accounting for heteroscedasticity, autocorrelation, etc. Note that if you see "striped" lines of residuals, that is an artifact of having an integer valued (vs a real valued) response variable.

Partial Dependence Plots

Partial dependence plot (PDP) gives a graphical depiction of the marginal effect of a variable on the response. The effect of a variable is measured in change in the mean response. PDP assumes independence between the feature for which is the PDP computed and the rest.

Individual Conditional Expectation

An Individual Conditional Expectation (ICE) plot gives a graphical depiction of the marginal effect of a variable on the response. ICE plots are similar to partial dependence plots (PDP); PDP shows the average effect of a feature while ICE plot shows the effect for a single instance. This function will plot the effect for each decile. In contrast to the PDP, ICE plots can provide more insight, especially when there is stronger feature interaction.